Computable Bayesian Compression for Uniformly Discretizable Statistical Models

DOI10.1007/978-3-642-04414-4_9zbMath1262.68053OpenAlexW1479984088MaRDI QIDQ3648742

Łukasz Dębowski

Publication date: 1 December 2009

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://ir.cwi.nl/pub/14595

Mathematics Subject Classification ID

Bayesian inference (62F15) Algorithmic information theory (Kolmogorov complexity, etc.) (68Q30)

Related Items (1)

Bayesian definition of random sequences with respect to conditional probabilities

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On a definition of random sequences with respect to conditional probability
• Data compression and histograms
• The consistency of the BIC Markov order estimator.
• Iterated logarithmic expansions of the pathwise code lengths for exponential families
• Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
• Von Mises' definition of random sequences reconsidered
• Universal codeword sets and representations of the integers
• Minimum description length induction, Bayesianism, and Kolmogorov complexity
• The minimum description length principle in coding and modeling

This page was built for publication: Computable Bayesian Compression for Uniformly Discretizable Statistical Models